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Previously in series: Lawful Uncertainty
You may have noticed a certain trend in recent posts: I’ve been arguing that randomness hath no power, that there is no beauty in entropy, nor yet strength from noise.
If one were to formalize the argument, it would probably run something like this: that if you define optimization as previously suggested, then sheer randomness will generate something that seems to have 12 bits of optimization, only by trying 4096 times; or 100 bits of optimization, only by trying 1030 times.
This may not sound like a profound insight, since it is true by definition. But consider – how many comic books talk about “mutation” as if it were a source of power? Mutation is random. It’s the selection part, not the mutation part, that explains the trends of evolution.
Or you may have heard people talking about “emergence” as if it could explain complex, functional orders. People will say that the function of an ant colony emerges – as if, starting from ants that had been selected only to function as solitary individuals, the ants got together in a group for the first time and the ant colony popped right out. But ant colonies have been selected on as colonies by evolution. Optimization didn’t just magically happen when the ants came together.
And you may have heard that certain algorithms in Artificial Intelligence work better when we inject randomness into them.
Is that even possible? How can you extract useful work from entropy?
But it is possible in theory, since you can have things that are anti-optimized. Say, the average state has utility -10, but the current state has an unusually low utility of -100. So in this case, a random jump has an expected benefit. If you happen to be standing in the middle of a lava pit, running around at random is better than staying in the same place. (Not best, but better.)
A given AI algorithm can do better when randomness is injected, provided that some step of the unrandomized algorithm is doing worse than random.
Continue reading "Worse Than Random" »
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